code: def testAgentTrajectoryTrain(self):
      agent = ddpg_agent.DdpgAgent(
          self._time_step_spec,
          self._action_spec,
          actor_network=self._bounded_actor_net,
          critic_network=self._critic_net,
          actor_optimizer=tf.compat.v1.train.AdamOptimizer(0.001),
          critic_optimizer=tf.compat.v1.train.AdamOptimizer(0.001),
      )
  
      trajectory_spec = trajectory.Trajectory(
          step_type=self._time_step_spec.step_type,
          observation=self._time_step_spec.observation,
          action=self._action_spec,
          policy_info=(),
          next_step_type=self._time_step_spec.step_type,
          reward=tensor_spec.BoundedTensorSpec(
              [], tf.float32, minimum=0.0, maximum=1.0, name="reward"
          ),
          discount=self._time_step_spec.discount,
      )
  
      sample_trajectory_experience = tensor_spec.sample_spec_nest(
          trajectory_spec, outer_dims=(3, 2)
      )
      agent.train(sample_trajectory_experience)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from tf_agents.agents.ddpg import ddpg_agent
  from tf_agents.networks import network
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import policy_step
  from tf_agents.trajectories import time_step
  from tf_agents.trajectories import trajectory
  from tf_agents.utils import common
  from tf_agents.utils import test_utils
label_desc: Use image node_id 6 for calling the DdpgAgentTest obj's underlying member method code with example usage: obj.testAgentTrajectoryTrain() without return types